In many sectors (for example, commercial, defense, etc.), information flows are becoming increasingly dynamic, voluminous and cross-organizational. For instance, in a social media information flow network, information from (un)trusted sources (for example, annotations on photos, user blogs, etc.) may be dynamically fused together (for example, by Web mashup applications) to construct information products (for example, Really Simple Syndication (RSS) feeds).
In such scenarios, a decision maker (a human or a software agent alike) is faced with the challenge of examining large volumes of information originating from heterogeneous sources with the goal of ascertaining trust in various pieces of information. A common data model subsumed by several trust computation models is the ability of an entity (for example, an information source) to assign a numeric trust score to another entity. In existing approaches, such pair-wise numeric ratings contribute to a (dis)similarity score (for example, based on L1 norm, L2 norm, cosine distance, etc.) which can be used to compute personalized trust scores or recursively propagated throughout the network to compute global trust scores.
A pair-wise numeric score-based data model, however, may impose severe limitations in several real-world applications. For example, suppose that information sources {S1, S2, S3} assert axioms φ1=all men are mortal, φ2=Socrates is a man and φ3=Socrates is not mortal, respectively. While there is an obvious conflict when all three axioms are put together, note that: (i) there is no pair-wise conflict, and (ii) there is no obvious numeric measure that captures (dis)similarity between two information sources.
This problem can become even more challenging because of uncertainty associated with real-world data and applications. Uncertainty can manifest itself in several diverse forms: from measurement errors (for example, sensor readings) and stochasticity in physical processes (for example, weather conditions) to reliability/trustworthiness of data sources. Regardless of its nature, a probabilistic measure for uncertainty can be adopted. Reusing the Socrates example above, each information source Si may assert the axiom φi with a certain probability pi=0.6. Further, probabilities associated with various axioms need not be (statistically) independent. For instance, an information source may assert that an axiom φ1 holds with probability p1 conditioned on the fact the axiom φ2 holds; the axiom φ2 may hold independent of any other axiom with probability p2. In such situations, a challenge includes developing trust computation models for rich (beyond pair-wise numeric ratings) and uncertain (probabilistic) information.